Improving the performance of P300-based BCIs by mitigating the effects of stimuli-related evoked potentials through regularized spatial filtering

Mobaien, A., Boostani, R. and Sanei, S. ORCID: 0000-0002-3437-2801, 2024. Improving the performance of P300-based BCIs by mitigating the effects of stimuli-related evoked potentials through regularized spatial filtering. Journal of Neural Engineering, 21 (1): 016023. ISSN 1741-2560

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Abstract

Objective: the P300-based brain-computer interface (BCI) establishes a communication channel between the mind and a computer by translating brain signals into commands. These systems typically employ a visual oddball paradigm, where different objects (linked to specific commands) are randomly and frequently intensified. Upon observing the target object, users experience an elicitation of a P300 event-related potential in their electroencephalography (EEG). However, detecting the P300 signal can be challenging due to its very low signal-to-noise ratio (SNR), often compromised by the sequence of visual evoked potentials (VEPs) generated in the occipital regions of the brain in response to periodic visual stimuli. While various approaches have been explored to enhance the SNR of P300 signals, the impact of VEPs has been largely overlooked. The main objective of this study is to investigate how VEPs impact P300-based BCIs. Subsequently, the study aims to propose a method for EEG spatial filtering to alleviate the effect of VEPs and enhance the overall performance of these BCIs.

Approach: Our approach entails analyzing recorded EEG signals from visual P300-based BCIs through temporal, spectral, and spatial analysis techniques to identify the impact of VEPs. Subsequently, we introduce a regularized version of the xDAWN algorithm, a well-established spatial filter known for enhancing single-trial P300s. This aims to simultaneously enhance P300 signals and suppress VEPs, contributing to an improved overall signal quality.

Main results: analyzing EEG signals shows that VEPs can significantly contaminate P300 signals, resulting in a decrease in the overall performance of P300-based BCIs. However, our proposed method for simultaneous enhancement of P300 and suppression of VEPs demonstrates improved performance in P300-based BCIs. This improvement is verified through several experiments conducted with real P300 data.

Significance: this study focuses on the effects of VEPs on the performance of P300-based BCIs, a problem that has not been adequately addressed in previous studies. It opens up a new path for investigating these BCIs. Moreover, the proposed spatial filtering technique has the potential to further enhance the performance of these systems.

Item Type:	Journal article
Publication Title:	Journal of Neural Engineering
Creators:	Mobaien, A., Boostani, R. and Sanei, S.
Publisher:	IOP Publishing
Date:	February 2024
Volume:	21
Number:	1
ISSN:	1741-2560
Identifiers:	Number Type 10.1088/1741-2552/ad2495 DOI 1897218 Other
Rights:	This is the Accepted Manuscript version of an article accepted for publication in Journal of Neural Engineering. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.1088/1741-2552/ad2495
Divisions:	Schools > School of Science and Technology
Record created by:	Jeremy Silvester
Date Added:	24 May 2024 09:16
Last Modified:	24 May 2024 09:16
URI:	https://irep.ntu.ac.uk/id/eprint/51474

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